SoroushVahidi/scidocs huggingface dataset:
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks
(2015)

Large multilayer neural networks trained with backpropagation have recently achieved state-ofthe-art results in a wide range of problems. However, using backprop for neural net learning still has some disadvantages, e.g., having to tune a large number of hyperparameters to the data, lack of calibrated probabilistic predictions, and a tendency to overfit the training data. In principle, the Bayesian approach to learning neural networks does not have these problems. However, existing Bayesian techniques lack scalability to large dataset and network sizes. In this work we present a novel scalable method for learning Bayesian neural networks, called probabilistic backpropagation (PBP). Similar to classical backpropagation, PBP works by computing a forward propagation of probabilities through the network and then doing a backward computation of gradients. A series of experiments on ten real-world datasets show that PBP is significantly faster than other techniques, while offering competitive predictive abilities. Our experiments also show that PBP provides accurate estimates of the posterior variance on the network weights.
Query Segmentation and Resource Disambiguation Leveraging Background Knowledge
(2012)

Accessing the wealth of structured data available on the Data Web is still a key challenge for lay users. Keyword search is the most convenient way for users to access information (e.g., from data repositories). In this paper we introduce a novel approach for determining the correct resources for user-supplied keyword queries based on a hidden Markov model. In our approach the user-supplied query is modeled as the observed data and the background knowledge is used for parameter estimation. Instead of learning parameter estimation from training data, we leverage the semantic relationships between data items for computing the parameter estimations. In order to maximize accuracy and usability, query segmentation and resource disambiguation are mutually tightly interwoven. First, an initial set of potential segmentations is obtained leveraging the underlying knowledge base; then the final correct set of segments is determined after the most likely resource mapping was computed using a scoring function. While linguistic methods like named entity, multi-word unit recognition and POS-tagging fail in the case of an incomplete sentences (e.g. for keyword-based queries), we will show that our statistical approach is robust with regard to query expression variance. Our experimental results when employing the hidden Markov model for resource identification in keyword queries reveal very promising results.
Humanoid locomotion on uneven terrain using the time-varying divergent component of motion
(2014)

This paper presents a framework for dynamic humanoid locomotion on uneven terrain using a novel time-varying extension to the Divergent Component of Motion (DCM). By varying the natural frequency of the DCM, we are able to achieve generic CoM height trajectories during stepping. The proposed planning algorithm computes admissible DCM reference trajectories given desired ZMP plans for single and double support. This is accomplished using reverse-time integration of the discretized DCM dynamics over a finite time horizon. To account for discontinuities during replanning, linear Model Predictive Control (MPC) is implemented over a short preview window. DCM tracking control is achieved using a time-varying proportional-integral controller based on the Virtual Repellent Point (VRP). The effectiveness of the combined approach is verified in simulation using a 30-DoF model of THOR, a compliant torque-controlled humanoid.
From Hunt the Wumpus to EverQuest: Introduction to Quest Theory
(2005)

The paper will explore how the landscape types and the quest types are used in various games, how they structure the gameplay, how they act as bones for the game-content (graphics, dialogue, sound) and how they sometimes form the base on which a story is imposed and related to the player. The question then becomes, how does the quest structure influence the story structure? How do the limitations of the quest combinations limit the kinds of story that are possible? How rich can the imposed story be, without breaking the gameplay? Are landscape and quest-structure the dominant factors in quest game design, to which the story-ambitions must defer? The main thesis of the paper is that if we understand the powerful but simple structure the grammar of quests (how they work, how they are used) we can understand both the limits and the potential of these kinds of games.
Two practical man-in-the-middle attacks on Bluetooth secure simple pairing and countermeasures
(2010)

We propose two new Man-In-The-Middle (MITM) attacks on Bluetooth Secure Simple Pairing (SSP). The attacks are based on the falsification of information sent during the input/output capabilities exchange and also the fact that the security of the protocol is likely to be limited by the capabilities of the least powerful or the least secure device type. In addition, we devise countermeasures that render the attacks impractical, as well as improvements to the existing Bluetooth SSP in order to make it more secure. Moreover, we provide a comparative analysis of the existing MITM attacks on Bluetooth.
Optimizing orthodontic treatment in patients taking bisphosphonates for osteoporosis.
(2009)

Bisphosphonates have unique pharmacological characteristics unlike those of any other drug group. Millions of adults take oral bisphosphonates for long-term treatment of osteoporosis and osteopenia; some of these people will most likely also seek orthodontic treatment. Adverse dental effects from bisphosphonates have been reported, including decreased tooth movement, impaired bone healing, and osteonecrosis in the mandible and the maxilla. Osteonecrosis has been rarely observed after bisphosphonate use for osteoporosis. However, adverse drug effects might occur more frequently in orthodontic patients, and they would probably be noted before the end-stage pathology of osteonecrosis. Adverse effects during orthodontic treatment, including decreased tooth movement, could last for years after the drug therapy is stopped. Successful orthodontic treatment requires optimal bone healing to prevent excessive tooth mobility. Bisphosphonates appear to have 2 bone elimination rates--a fast elimination of weeks from the bone surface and a slow elimination of years after incorporation into the bone structure. This article presents methods to clinically and radiographically monitor orthodontic patients who are taking oral bisphosphonates. Efforts to minimize adverse effects and optimize orthodontic procedures with physician-approved drug holidays are discussed. The orthodontic treatment results of 3 patients who received bisphosphonate therapy are reported.
Use of e-books in an academic and research environment: A case study from the Indian Institute of Science
(2006)

Purpose: E-books are not as popular as other types of e-publications, such as e-journals and e-newspapers. The possible reasons for this could be because the technology for creating/accessing e-books (both hardware and software) is not yet matured, users’ perspectives about e-books need to be changed and the awareness of e-books needs to be raised. The purpose of this study is to investigate the use and usability of e-books from users’ perspectives in an academic and research environment.
Robust Visual Tracking with Deep Convolutional Neural Network Based Object Proposals on PETS
(2016)

Tracking by detection based object tracking methods encounter numerous complications including object appearance changes, size and shape deformations, partial and full occlusions, which make online adaptation of classifiers and object models a substantial challenge. In this paper, we employ an object proposal network that generates a small yet refined set of bounding box candidates to mitigate the this object model refitting problem by concentrating on hard negatives when we update the classifier. This helps improving the discriminative power as hard negatives are likely to be due to background and other distractions. Another intuition is that, in each frame, applying the classifier only on the refined set of object-like candidates would be sufficient to eliminate most of the false positives. Incorporating an object proposal makes the tracker robust against shape deformations since they are handled naturally by the proposal stage. We demonstrate evaluations on the PETS 2016 dataset and compare with the state-of-theart trackers. Our method provides the superior results.
Design and Sensitivity Analysis of Highly Compact Comparator for Ku-Band Monopulse Radar
(2006)

This paper gives the design of a highly compact comparator at a Ku-band frequency and presents analysis results of the comparator for the fabrication inaccuracies. First an unconventional magic-t using a nonstandard waveguide is designed at 15.50 GHz. To reduce the volume occupied by the magic-t, its E-arm (or difference port) is kept parallel to the plane of two inputs of the magic-t instead of perpendicular to them as is done in a convention magic-t. The sum and the difference ports of the above folded magic-t are then matched using inductive windows at 15.50 GHz. Keeping the required location of the outputs of the comparator in mind, four of these matched folded magic-ts are suitably interconnected to design a highly compact comparator. The effects of the fabrication errors in the waveguide and matching elements dimensions on the centre frequency, magnitude and phase response of the comparator are also analyzed and presented.
5G 3GPP-Like Channel Models for Outdoor Urban Microcellular and Macrocellular Environments
(2016)

For the development of new 5G systems to operate in bands up to 100 GHz, there is a need for accurate radio propagation models at these bands that currently are not addressed by existing channel models developed for bands below 6 GHz. This document presents a preliminary overview of 5G channel models for bands up to 100 GHz. These have been derived based on extensive measurement and ray tracing results across a multitude of frequencies from 6 GHz to 100 GHz, and this document describes an initial 3D channel model which includes: 1) typical deployment scenarios for urban microcells (UMi) and urban macrocells (UMa), and 2) a baseline model for incorporating path loss, shadow fading, line of sight probability, penetration and blockage models for the typical scenarios. Various processing methodologies such as clustering and antenna decoupling algorithms are also presented.
Breathing at a rate of 5.5 breaths per minute with equal inhalation-to-exhalation ratio increases heart rate variability.
(2014)

OBJECTIVES
Prior studies have found that a breathing pattern of 6 or 5.5 breaths per minute (bpm) was associated with greater heart rate variability (HRV) than that of spontaneous breathing rate. However, the effects of combining the breathing rate with the inhalation-to-exhalation ratio (I:E ratio) on HRV indices are inconsistent. This study aimed to examine the differences in HRV indices and subjective feelings of anxiety and relaxation among four different breathing patterns.


METHODS
Forty-seven healthy college students were recruited for the study, and a Latin square experimental design with a counterbalance in random sequences was applied. Participants were instructed to breathe at two different breathing rates (6 and 5.5 breaths) and two different I:E ratios (5:5 and 4:6). The HRV indices as well as anxiety and relaxation levels were measured at baseline (spontaneous breathing) and for the four different breathing patterns.


RESULTS
The results revealed that a pattern of 5.5 bpm with an I:E ratio of 5:5 produced a higher NN interval standard deviation and higher low frequency power than the other breathing patterns. Moreover, the four different breathing patterns were associated with significantly increased feeling of relaxation compared with baseline.


CONCLUSION
The study confirmed that a breathing pattern of 5.5 bpm with an I:E ratio of 5:5 achieved greater HRV than the other breathing patterns. This finding can be applied to HRV biofeedback or breathing training in the future.
Data transmission via GSM voice channel for end to end security
(2015)

Global System for Mobile Communications (GSM) technology still plays a key role because of its availability, reliability and robustness. Recently, additional consumer applications are proposed in which GSM is used as a backup or data transmission service. Unfortunately sending data via GSM channel is a challenging task since it is speech sensitive and suppresses other forms of signals. In this paper, a systematic method is proposed to develop a modem that transmits data over GSM voice channel (DoGSMV) using speech like (SL) symbols. Unlike the previous approaches an artificial search space is produced to find best SL symbols and analyses by synthesis (AbyS) method is introduced for parameter decoding. As a result 1.6 kbps simulation data rate is achieved when wireless communication errors are ignored.
Frequency-Reconfigurable Low-Profile Circular Monopolar Patch Antenna
(2014)

A novel frequency-reconfigurable antenna is presented based on a circular monopolar patch antenna. The antenna comprises of a center-fed circular patch surrounded by four sector-shaped patches. Eight varactor diodes are introduced to bridge the gaps between the circular patch and the sector-shaped patches. By changing the reverse bias voltage of the varactor diodes, the antenna can be switched in the operating frequency. A fully functional prototype is developed and tested, which exhibits a continuously tunable frequency band from 1.64 GHz to 2.12 GHz. The measured efficiency rises from about 45% to about 85% as the operating frequency increases from 1.64 GHz to 2.12 GHz. Stable monopole like radiation patterns are achieved at all operating frequencies. In addition, the antenna owns a low-profile structure (0.04 free-space wavelengths at 1.9 GHz). The frequency selective feature and stable radiation patterns make the antenna potentially suitable for future wireless communication systems, such as cognitive radio.
Three-dimensional soft-tissue and hard-tissue changes in the treatment of bimaxillary protrusion.
(2013)

INTRODUCTION
Facial convexity related to bimaxillary protrusion is prevalent in many populations. Underlying skeletal protrusion combined with increased dentoalveolar protrusion contributes to facial muscle imbalance and lip incompetence, which is undesirable for many patients. In this study, we evaluated the relationship between soft-tissue and hard-tissue changes in an orthodontically treated Asian population.


METHODS
Twenty-four consecutive adult Asian patients (mean age, 24 years), diagnosed with severe bimaxillary dentoalveolar protrusion, were evaluated using pretreatment and posttreatment cone-beam computed tomography. The patients were treated with 4 first premolar extractions followed by anterior retraction with either skeletal or intraoral anchorage. Serial cone-beam computed tomography radiographs were registered on the entire cranial base and fossa. Soft-tissue and hard-tissue changes were determined through landmark displacement and color mapping.


RESULTS
Upper lip retraction was concentrated between the nasolabial folds and commissures. Lower lip retraction was accompanied by significant redistribution of soft tissues at pogonion. Soft-tissue changes correlated well with regional facial muscle activity. Significant retractions (2-4 mm) of the soft tissues occurred beyond the midsagittal region. Use of skeletal anchorage resulted in 1.5 mm greater lower lip retraction than intraoral anchorage, with greater retraction of the maxillary and mandibular incisor root apices.


CONCLUSIONS
Profound soft-tissue changes accompanied retraction of the anterior dentition with both treatment modalities.
Management of a Large Frontal Encephalocoele With Supraorbital Bar Remodeling and Advancement.
(2017)

Of all the craniofacial abnormalities, facial clefts are the most disfiguring. Facial clefts are classified according to the affected anatomical area as described by Tessier. Through this classification, the location and extent of the cleft can be designated numerically.A 2-month-old male infant was referred to authors' craniofacial unit, from a hospital in a rural province of South Africa, with a problem of a supranasal encephalocoele. Bilateral raised eyebrows were noted as was a right-sided upper lid central third coloboma. Computed tomography and magnetic resonance imaging scans confirmed the presence of a supranasal encephalocoele with a large frontal bone defect and splayed nasal bones. Bilateral enlarged orbits were noted with tented orbital roofs that we classified as Tessier number 10 facial clefts. The child was booked for an encephalocoele excision and calvarial reconstruction at 4 months of age.As a result of the encephalocoele, the supraorbital bar with its adjacent nasal bones was cleaved in 2, resulting in a significant frontal bone defect. Osteotomies were performed to remove the supraorbital bar and nasal bones from the calvarium. The supraorbital bar segment was remodeled and plated with absorbable poly-L-lactic acid plates. Osteotomies of the nasal bones allowed them to be united centrally, also with absorbable plates. This entire construct was transferred and secured to the calvarium, but in a more caudal position thereby obliterating the frontal bone and Tessier number 10 facial cleft defects with a naturally contoured construct.
Parallel Cat Swarm Optimization
(2008)

We investigate a parallel structure of cat swarm optimization (CSO) in this paper, and we call it parallel cat swarm optimization (PCSO). In the experiments, we compare particle swarm optimization (PSO) with CSO and PCSO. The experimental results indicate that both CSO and PCSO perform well. Moreover, PCSO is an effective scheme to improve the convergent speed of cat swarm optimization in case the population size is small and the whole iteration is less.
WiFi-Nano: reclaiming WiFi efficiency through 800 ns slots
(2011)

The increase in WiFi physical layer transmission speeds from 1~Mbps to 1 Gbps has reduced transmission times for a 1500 byte packet from 12 ms to 12 us. However, WiFi MAC overheads such as channel access and acks have not seen similar reductions and cumulatively contribute about 150 us on average per packet. Thus, the efficiency of WiFi has deteriorated from over 80% at 1 Mbps to under 10% at 1 Gbps.
 In this paper, we propose WiFi-Nano, a system that uses 800 ns slots} to significantly improve WiFi efficiency. Reducing slot time from 9 us to 800 ns makes backoffs efficient, but clear channel assessment can no longer be completed in one slot since preamble detection can now take multiple slots. Instead of waiting for multiple slots for detecting preambles, nodes speculatively transmit preambles as their backoff counters expire, while continuing to detect premables using self-interference cancellation. Upon detection of preambles from other transmitters, nodes simply abort their own preamble transmissions, thereby allowing the earliest transmitter to succeed. Further, receivers speculatively transmit their ack preambles at the end of packet reception, thereby reducing ack overhead. We validate the effectiveness of WiFi-Nano through implementation on an FPGA-based software defined radio platform, and through extensive simulations, demonstrate efficiency gains of up to 100%.
Who, What, When, and Where: Multi-Dimensional Collaborative Recommendations Using Tensor Factorization on Sparse User-Generated Data
(2015)

Given the abundance of online information available to mobile users, particularly tourists and weekend travelers, recommender systems that effectively filter this information and suggest interesting participatory opportunities will become increasingly important. Previous work has explored recommending interesting locations; however, users would also benefit from recommendations for activities in which to participate at those locations along with suitable times and days. Thus, systems that provide collaborative recommendations involving multiple dimensions such as location, activities and time would enhance the overall experience of users.The relationship among these dimensions can be modeled by higher-order matrices called tensors which are then solved by tensor factorization. However, these tensors can be extremely sparse. In this paper, we present a system and an approach for performing multi-dimensional collaborative recommendations for Who (User), What (Activity), When (Time) and Where (Location), using tensor factorization on sparse user-generated data. We formulate an objective function which simultaneously factorizes coupled tensors and matrices constructed from heterogeneous data sources. We evaluate our system and approach on large-scale real world data sets consisting of 588,000 Flickr photos collected from three major metro regions in USA. We compare our approach with several state-of-the-art baselines and demonstrate that it outperforms all of them.
A System to Verify Network Behavior of Known Cryptographic Clients
(2017)

Numerous exploits of client-server protocols and applications involve modifying clients to behave in ways that untampered clients would not, such as crafting malicious packets. In this paper, we develop a system for verifying in near real-time that a cryptographic client’s message sequence is consistent with its known implementation. Moreover, we accomplish this without knowing all of the client-side inputs driving its behavior. Our toolchain for verifying a client’s messages explores multiple candidate execution paths in the client concurrently, an innovation useful for aspects of certain cryptographic protocols such as message padding (which will be permitted in TLS 1.3). In addition, our toolchain includes a novel approach to symbolically executing the client software in multiple passes that defers expensive functions until their inputs can be inferred and concretized. We demonstrate client verification on OpenSSL and BoringSSL to show that, e.g., Heartbleed exploits can be detected without Heartbleed-specific filtering and within seconds of the first malicious packet. On legitimate traffic our verification keeps pace with Gmail-shaped workloads, with a median lag of 0.85s.
Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review
(2018)

This paper investigates recent research on active learning for (geo) text and image classification, with an emphasis on methods that combine visual analytics and/or deep learning. Deep learning has attracted substantial attention across many domains of science and practice, because it can find intricate patterns in big data; but successful application of the methods requires a big set of labeled data. Active learning, which has the potential to address the data labeling challenge, has already had success in geospatial applications such as trajectory classification from movement data and (geo) text and image classification. This review is intended to be particularly relevant for extension of these methods to GISience, to support work in domains such as geographic information retrieval from text and image repositories, interpretation of spatial language, and related geo-semantics challenges. Specifically, to provide a structure for leveraging recent advances, we group the relevant work into five categories: active learning, visual analytics, active learning with visual analytics, active deep learning, plus GIScience and Remote Sensing (RS) using active learning and active deep learning. Each category is exemplified by recent influential work. Based on this framing and our systematic review of key research, we then discuss some of the main challenges of integrating active learning with visual analytics and deep learning, and point out research opportunities from technical and application perspectives—for application-based opportunities, with emphasis on those that address big data with geospatial components.